Physiologically Responsive AI for Detecting and Managing Driver Impairment States in Semi-Autonomous Vehicles

Affiliations:	College of Engineering and Computer Science
Team Leader:	Ancuta Margondai Ancuta.Margondai@ucf.edu Modeling and Simulation PhD
Faculty Mentor:	Mustapha Mouloua, PhD
Team Size:	50
Open Spots:	4
Team Member Qualifications:	Essential Requirements: CITI Training Certification (or willingness to complete immediately upon acceptance) Required before working with participants; ~3–4 hours, available online Commitment to In-Person Lab Work Regular, scheduled sessions involving human participants and specialized equipment Reliable attendance and scheduling flexibility required Professional Conduct Punctual, dependable, and culturally sensitive Maintains confidentiality and follows ethical research protocols Willingness to Learn Open to training in ECG, HRV, and physiological data collection Eager to develop skills in Python, SPSS, and human-AI interaction research Receptive to feedback and continuous growth All Disciplines Welcome: We welcome students from any major. Comprehensive training is provided in: Physiological sensor setup (ECG, facial recognition) Participant recruitment and data collection Data analysis (Python, SPSS) and research ethics AI system monitoring, driving simulator operation, and experimental protocols Required for Application: CITI Training — Social & Behavioral Research (SBR) Basic Course Must be completed before working with participants (~3–4 hours, free online) Certificate must be submitted upon acceptance Research Commitment Available for in-person, scheduled data collection sessions Reliable attendance is non-negotiable — sessions involve human participants and live physiological equipment Professional Standards Maintains confidentiality and follows IRB protocol (IRB #8420) Respectful, punctual, and culturally sensitive with diverse participant populations Basic Computer Literacy Comfortable learning new software; prior Python or SPSS experience is a plus but not required.
Description:	This research investigates how artificial intelligence systems can monitor a driver's physiological state in real time, using heart rate patterns and facial cues, to detect stress, fatigue, or cognitive overload. The system then adapts the level of information it shares with the driver based on those states. The goal is to design human-AI partnerships in semi-autonomous vehicles that are safer, more transparent, and better aligned with human cognitive capacity.